A three-component dynamical index of consciousness-related neural organisation

Summary

A new composite index that combines three properties of brain activity—scale-free temporal organization, cross-frequency organization, and metastable flexibility in large-scale synchronization—separates conscious from non-conscious synthetic brain states without overlap and distinguishes wakefulness from N2 and REM sleep in 30 healthy adults using two-channel EEG recordings. The index captures organized dynamical complexity rather than raw signal complexity alone and remains stable across sensitivity and Monte Carlo analyses. The framework is not tied to any single theory of consciousness and may be applicable to anesthesia, disorders of consciousness, and basic consciousness research.

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Abstract

Quantifying consciousness from brain activity remains a major challenge in neuroscience and clinical practice. Many existing EEG measures focus on a single feature of neural activity, such as complexity, synchrony, or spectral structure, but no single feature appears sufficient across different brain states. We introduce a composite dynamical framework that combines three complementary properties of brain activity, i.e., scale-free temporal organisation, cross-frequency organisation, and metastable flexibility in large-scale synchronisation. These components are normalised and combined into a single index designed to capture organised dynamical complexity rather than raw signal complexity alone. We test the framework in both synthetic and empirical settings. In a generative model of nine EEG-like brain states, including wakefulness, dreaming, anaesthesia, non-conscious states, and seizure states, the index separates the synthetic conscious and non-conscious classes without overlap and remains stable across ablation, sensitivity, and Monte Carlo analyses. We then apply the framework to two-channel Sleep-EDF recordings from 30 healthy adults, where it provides a proof-of-principle subject-level separation of wakefulness from N2 and REM sleep. The framework is dynamical-systems-inspired and is not committed to any single theory of consciousness, making it compatible with a range of theoretical perspectives. With further validation, the framework may be applicable across multichannel brain recordings, including anaesthesia, disorders of consciousness, and basic consciousness-research settings.

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